Transformation of Electronic Health Records and Questionnaire Data to OMOP CDM: A Feasibility Study Using SG_T2DM Dataset

Background Diabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on health care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients' data from diverse sources and use scientific discovery to inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen as the framework for integrating data with disparate formats. Objective The study aimed to evaluate the feasibility of converting Singapore based data source, comprising of electronic health records (EHR), cognitive and depression assessment questionnaire data to OMOP CDM standard. Additionally, we also validate whether our OMOP CDM instance is fit for the purpose of research by executing a simple treatment pathways study using Atlas, a graphical user interface tool to conduct analysis on OMOP CDM data as a proof of concept. Methods We used de-identified EHR, cognitive, and depression assessment questionnaires data from a tertiary care hospital in Singapore to convert it to version 5.3.1 of OMOP CDM standard. We evaluate the OMOP CDM conversion by (1) assessing the mapping coverage (that is the percentage of source terms mapped to OMOP CDM standard); (2) local raw dataset versus CDM dataset analysis; and (3) Implementing Harmonized Intrinsic Data Quality Framework using an open-source R package called Data Quality Dashboard. Results The content coverage of OMOP CDM vocabularies is more than 90% for clinical data, but only around 11% for questionnaire data. The comparison of characteristics between source and target data returned consistent results and our transformed data did not pass 38 (1.4%) out of 2,622 quality checks. Conclusion Adoption of OMOP CDM at our site demonstrated that EHR data are feasible for standardization with minimal information loss, whereas challenges remain for standardizing cognitive and depression assessment questionnaire data that requires further work.